The impact of social network position on innovation has been widely confirmed in past studies. However, research on the time-lag structure of the impact is still insufficient. Within the time window 2010 to 2017, this study constructs a two-mode social network between Chinese listed companies and other participants. To analyze the lag structure of the effect of social network position on innovation, this study uses a panel negative binomial regression model transformed by the Almon polynomial. The results show that a firm does need an advantageous past social network position for innovation. Previous local and global centrality in a social network has a different influence on innovation. For the local centrality indices, degree centrality has a positive impact in the short-term, but has a negative impact in the long-term; the impact of betweenness centrality is not significant in the short-term and is negative in the long run. For the global centrality indices, closeness centrality has a positive influence that decreases with the increase of the time-lag. At the same time, using the method of necessary condition analysis (NCA), this study calculates the bottleneck for a given innovation level. Finally, based on these research conclusions, the theoretical implications and management practice implications are summarized.
How Does A Firm’s Previous Social Network Position Affect Innovation? Evidence from Chinese Listed Companies
